Without the atlas each researcher could spend a lifetime trying to gather complete gene-expression data for his or her work.
因为如果没有图谱，每个研究者为了他或他的研究，可能会花费一生的时间来搜集完整的基因表达数据。
The first step is to cluster gene expression data.
第一步是聚类基因表达数据。
There are lots of cluster methods applied to the analysis of gene expression data.
常用于基因表达数据分析的聚类方法有很多。
Classification for gene expression data is an important research filed in bioinformatics.
对基因表达谱进行分类是生物信息学中一个重要的研究领域。
Objective We investigate the use of random forests for classification of gene expression data.
目的探讨随机森林算法在基因表达数据分类研究中的应用。
The theory and method of neural network ensemble were studied in the given gene expression data.
以一个典型的微阵列基因表达数据集为背景研究了神经网络集成的理论和方法。
Through seriate genome-wide mRNA expression data, similarity between two genes could be measured.
通过分析酵母细胞的大规模基因表达谱数据，获得不同基因间表达的相关性。
The analysis and research on gene expression data is an important research area of bioinformatics.
基因表达数据的分析和研究是生物信息学中重要的研究课题。
Objective Discuss the condition and the effect of SVM in the classification of gene expression data.
目的探讨支持向量机在基因表达数据分类研究中的应用条件和效果。
There is some obvious inaccuracy of gene expression in the experiment to obtain the gene expression data.
在基因表达谱数据获取过程中，基因表达谱数据含有较大的实验误差。
In our research, gene expression data of Saccharomyces cerevisiae is applied to construct regulatory network.
本研究中，酿酒酵母的基因表达数据被用来建立调控网络。
The list of genes was shifted with respect to the expression data, so that the one did not correspond with the other.
表中基因的一列相对于表达数据发生了调换，导致两项不相对应。
In this chapter we discuss one of the few abundant sources for temporal information, time series expression data.
在这一章中，我们讨论了时间信息的一些丰富来源中的一种，时间序列表达数据。
To search a new and effective method for feature extraction and classification based on microarray expression data.
基于微阵列表达数据，探索新的有效特征提取和分类方法。
One model is fuzzy cluster analysis of gene expression data based on a cluster validity measure named Xie-Beni index.
一种模型是基于有效性测度谢白尼指数的基因表达数据的模糊聚类分析。
Currently, cluster methods are used most frequently among the methods applied to the analysis of gene expression data.
目前对基因表达数据进行分析的各种方法中，聚类分析方法应用得最多。
With the extensive applications of DNA microarray technology, huge amounts of gene expression data have been generated.
随着基因芯片技术的广泛应用，产生了海量的基因表达数据。
The cluster analysis of gene expression data is an important means for discovering gene functions and regular to mechanisms.
基因表达谱数据的聚类分析对于研究基因功能和基因调控机制有重要意义。
The cluster analysis of gene expression data is an important means for discovering gene functions and regulatory mechanisms.
基因表达谱数据的聚类分析对于研究基因功能和基因调控机制有重要意义。
There is missing value in microarray experiments and it will affect the stability and precision of the expression data analysis.
在基因芯片实验中，数据缺失客观存在，并在一定程度上影响芯片数据后续分析结果的准确性。
Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks.
最后，我们提出了结合时间序列表达数据和静态数据来构建动态调控网络的方法。
In microarray experiments, the missing value does exist and somewhat affect the stability and precision of the expression data analysis.
在基因芯片实验中，数据缺失客观存在，并且在一定程度上会影响芯片数据后续分析结果的准确性。
According to the characteristics of gene expression data, a high accurate density-based clustering algorithm called DENGENE was proposed.
根据基因表达数据的特点，提出一种高精度的基于密度的聚类算法DENGENE。
In microarray experiments, the missing value does exist and somewhat affects the stability and precision of the expression data analysis.
在不增加实验次数的情况下，缺失值估计是降低缺失数据对后续分析影响的有效方法。
This thesis improves classification using gene expression data method in two aspects: feature selection and SVMs classification algorithm.
针对基于基因表达数据的分类，本文从特征基因选择和支持向量机分类算法两个方面进行了改进。
Another alleged error the researchers at the Anderson centre discovered was a mismatch in a table that compared genes to gene-expression data.
另一个据Anderson中心的研究员指出的错误是一张表中基因和其基因表达数据的不匹配。
Along with the research and extensive application of DNA chip technology, gene expression data analysis have become a hotspot in life science field.
随着DNA芯片技术的广泛应用，基因表达数据分析已成为生命科学的研究热点。
Then, the housekeeping gene was used to adjust the rest gene expression data in order to keep the correct rate of pre-analysis gene expression data.
再利用看家基因调整余下的基因表达数据，从而保证待分析的基因表达数据的正确率。
This thesis improves tumor samples classification of gene expression data in two aspects: classification algorithm and feature gene selection method.
针对基于基因表达数据的肿瘤样本分类，本文从分类算法和特征基因选取方法两个方面进行了改进。
This thesis improves tumor samples classification of gene expression data in two aspects: classification algorithm and feature gene selection method.
针对基于基因表达数据的肿瘤样本分类，本文从分类算法和特征基因选取方法两个方面进行了改进。
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